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baseline input parameters

• We are standardizing content for these sections
  • 6.6 Population Modeling or Analysis

Standards for these sections will not be developed at this time, but can be referenced:

6.1 Tabular Listing of All Population Health Studies
6.2 Tobacco Product Perception and Intention Study 
6.3 Behavioral Epidemiology (Observational) Study
6.4 Biomarker Epidemiology (Observational) Study
6.5 Health Risk Epidemiology (Observational) Study
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6.7 Postmarket Surveillance and Postmarket Study Plan or Protocol
6.8 Population Health Literature Review
6.9 Other Documents Relating to Research [911(d)(5)] or 910(b)(1)]
6.10 Referenced Literature

Tobacco products can lead to chronic health effects that can take decades to manifest (e.g., lung cancer can take 20+ years), which would require long-term studies to assess. Population models and simulations provide a desirable alternative for making estimates and predictions of likely impact on morbidity/mortality at the population level in the absence of empirical data. Mathematical, computational, and simulation models can also help guide regulatory activities such as new product authorizations and policy development. Such models take into consideration both users and nonusers of tobacco products and include cohort models, agent-based models, deterministic and stochastic systemic dynamic models, and static and dynamic social network models.
The objective of population modeling is to study the impact of tobacco products on the population as whole. Input parameters include demographic information, tobacco use transition probability and mortality and/or morbidity. The input parameters are typically derived from population-level sources (e.g., census data or other population-level surveys). However, depending on the objective, other sources of data may be used. When using these other sources, steps should be taken to ensure they are representative of the population.
Outputs of the model may include projections on morbidity/mortality and prevalence of use resulting from the impact of the desired objective of the model (such as new product authorizations or regulatory policy development).

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